{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ENG-KareemSaleh/ml-dl-projects/blob/main/ECG_Heartbeat_Classification_(1D_Conv).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJ3xXolvCla0",
        "outputId": "182fde38-7866-4ed9-8ad9-f344045568e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data source import complete.\n"
          ]
        }
      ],
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "import kagglehub\n",
        "shayanfazeli_heartbeat_path = kagglehub.dataset_download('shayanfazeli/heartbeat')\n",
        "\n",
        "print('Data source import complete.')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization, LeakyReLU, GlobalAveragePooling1D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report,accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.utils import resample"
      ],
      "metadata": {
        "id": "lTdkkzHNCzQR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(\"/kaggle/input/heartbeat/mitbih_train.csv\", header=None)\n",
        "df_test = pd.read_csv(\"/kaggle/input/heartbeat/mitbih_test.csv\", header=None)"
      ],
      "metadata": {
        "id": "W0qOd_J5C7r2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_counts = df_train.iloc[:, 187].value_counts().sort_index()\n",
        "labels = ['N', 'S', 'V', 'F', 'Q']\n",
        "counts = [label_counts.get(i, 0) for i in range(5)]\n",
        "total = sum(counts)\n",
        "percentages = [count / total * 100 for count in counts]\n",
        "\n",
        "colors = ['#D2042D', '#A8092D', '#7D0D2C', '#450C1C', '#0D0A0B']\n",
        "\n",
        "plt.figure(figsize=(7, 4.5))\n",
        "bars = plt.bar(labels, counts, color=colors, edgecolor='black', linewidth=1.5)\n",
        "\n",
        "for bar, pct, color in zip(bars, percentages, colors):\n",
        "    height = bar.get_height()\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, height + total * 0.01,\n",
        "             f'{pct:.1f}%', ha='center', va='bottom', color=color,\n",
        "             fontsize=12, fontweight='bold')\n",
        "\n",
        "plt.title('Distribution of ECG Heartbeat Classes', fontsize=15)\n",
        "plt.ylabel('Number of Samples')\n",
        "plt.ylim(0, max(counts) * 1.15)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "Nyo_mTvjC_iR",
        "outputId": "f007906e-4d89-432e-e719-ef69b0ef96da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x450 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "signal_cols = df_train.columns[:187]\n",
        "df_signals = df_train[signal_cols]\n",
        "df_labels = df_train[187]\n",
        "\n",
        "grouped_classes = df_signals.groupby(df_labels).mean()\n",
        "\n",
        "conditions = {\n",
        "    0: \"Normal beat (N)\",\n",
        "    1: \"Supraventricular ectopic beat (S)\",\n",
        "    2: \"Ventricular ectopic beat (V)\",\n",
        "    3: \"Fusion beat (F)\",\n",
        "    4: \"Unknown beat (Q)\"\n",
        "}\n",
        "\n",
        "fig, axes = plt.subplots(5, 1, figsize=(8, 10), sharex=True)\n",
        "\n",
        "for i in range(5):\n",
        "    signal = grouped_classes.loc[i].values\n",
        "    ax = axes[i]\n",
        "    ax.plot(signal, color='black', linewidth=1.5)\n",
        "    ax.set_title(conditions[i], fontsize=13)\n",
        "    ax.set_ylabel(\"Amplitude (mV)\", fontsize=11)\n",
        "    ax.grid(which='both', linestyle='--', linewidth=0.5, color='red', alpha=0.3)\n",
        "    ax.minorticks_on()\n",
        "\n",
        "axes[-1].set_xlabel(\"Time (ms)\", fontsize=12)\n",
        "\n",
        "fig.suptitle(\"Average ECG Waveform per Class\", fontsize=15)\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9to3jFKEDDWf",
        "outputId": "6d63b44f-d734-4147-dc91-82f4f71582fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1000 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train.iloc[:, :187].values\n",
        "y_train = df_train.iloc[:, 187].values.astype(int)\n",
        "\n",
        "X_test = df_test.iloc[:, :187].values\n",
        "y_test = df_test.iloc[:, 187].values.astype(int)\n"
      ],
      "metadata": {
        "id": "hcQ-uTXKDGOn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "n5S9_kP6DG_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bal = pd.DataFrame(X_train_scaled)\n",
        "df_bal['label'] = y_train\n",
        "\n",
        "max_count = df_bal['label'].value_counts().max()\n",
        "df_upsampled = pd.concat([\n",
        "    resample(df_bal[df_bal['label'] == i], replace=True, n_samples=max_count, random_state=42)\n",
        "    for i in range(5)\n",
        "])\n",
        "\n",
        "df_upsampled = df_upsampled.sample(frac=1, random_state=42)\n",
        "\n",
        "X_bal = df_upsampled.drop('label', axis=1).values\n",
        "y_bal = df_upsampled['label'].values.astype(int)\n",
        "\n",
        "# Reshape for Conv1D\n",
        "X_bal_cnn = X_bal.reshape(-1, 187, 1)\n",
        "X_test_cnn = X_test_scaled.reshape(-1, 187, 1)\n",
        "\n",
        "# Split for validation\n",
        "X_train_cnn, X_val_cnn, y_train_cnn, y_val_cnn = train_test_split(\n",
        "    X_bal_cnn, y_bal, test_size=0.2, stratify=y_bal, random_state=42\n",
        ")\n"
      ],
      "metadata": {
        "id": "DXPeWa4ODIlL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Dropout, Flatten, Dense, BatchNormalization, LeakyReLU\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Build CNN model\n",
        "model = Sequential([\n",
        "    Conv1D(32, 5, padding='same', input_shape=(187,1)),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.3),\n",
        "\n",
        "    Conv1D(64, 5, padding='same'),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.3),\n",
        "\n",
        "    Conv1D(128, 5, padding='same'),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.3),\n",
        "\n",
        "    Conv1D(256, 5, padding='same'),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.3),\n",
        "\n",
        "    Conv1D(512, 5, padding='same'),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.4),\n",
        "\n",
        "    Conv1D(512, 5, padding='same'),\n",
        "    BatchNormalization(), LeakyReLU(0.3), MaxPooling1D(2), Dropout(0.4),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(1024), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(512), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(256), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(128), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(64), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(32), BatchNormalization(), LeakyReLU(0.3), Dropout(0.5),\n",
        "    Dense(5, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile model\n",
        "model.compile(\n",
        "    optimizer=Adam(1e-3),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# Early stopping and LR scheduling\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3)\n",
        "\n",
        "# 🛠️ Manual class weights (adjust these if needed)\n",
        "class_weights_dict = {\n",
        "    0: 0.3,  # down-weight class 0 to reduce false negatives in minority classes\n",
        "    1: 2.0,\n",
        "    2: 1.0,\n",
        "    3: 2.5,\n",
        "    4: 1.0\n",
        "}\n",
        "\n",
        "# Train model\n",
        "model.fit(\n",
        "    X_train_cnn, y_train_cnn,\n",
        "    validation_data=(X_val_cnn, y_val_cnn),\n",
        "    epochs=10,\n",
        "    batch_size=128,\n",
        "    callbacks=[early_stop, reduce_lr],\n",
        "    verbose=1,\n",
        "    class_weight=class_weights_dict\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrHY8BchDb-t",
        "outputId": "92c2d51f-6a59-46c5-ece7-2f0ccf160f8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 21ms/step - accuracy: 0.6249 - loss: 0.9378 - val_accuracy: 0.8960 - val_loss: 0.3276 - learning_rate: 0.0010\n",
            "Epoch 2/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 15ms/step - accuracy: 0.8631 - loss: 0.2592 - val_accuracy: 0.9286 - val_loss: 0.2284 - learning_rate: 0.0010\n",
            "Epoch 3/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 16ms/step - accuracy: 0.9182 - loss: 0.1716 - val_accuracy: 0.9561 - val_loss: 0.1443 - learning_rate: 0.0010\n",
            "Epoch 4/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 14ms/step - accuracy: 0.9380 - loss: 0.1302 - val_accuracy: 0.9616 - val_loss: 0.1199 - learning_rate: 0.0010\n",
            "Epoch 5/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 14ms/step - accuracy: 0.9472 - loss: 0.1067 - val_accuracy: 0.9682 - val_loss: 0.1062 - learning_rate: 0.0010\n",
            "Epoch 6/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 14ms/step - accuracy: 0.9560 - loss: 0.0894 - val_accuracy: 0.9668 - val_loss: 0.1012 - learning_rate: 0.0010\n",
            "Epoch 7/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 15ms/step - accuracy: 0.9600 - loss: 0.0793 - val_accuracy: 0.9743 - val_loss: 0.0818 - learning_rate: 0.0010\n",
            "Epoch 8/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 14ms/step - accuracy: 0.9639 - loss: 0.0707 - val_accuracy: 0.9760 - val_loss: 0.0791 - learning_rate: 0.0010\n",
            "Epoch 9/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 14ms/step - accuracy: 0.9653 - loss: 0.0662 - val_accuracy: 0.9771 - val_loss: 0.0696 - learning_rate: 0.0010\n",
            "Epoch 10/10\n",
            "\u001b[1m2265/2265\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 15ms/step - accuracy: 0.9678 - loss: 0.0633 - val_accuracy: 0.9799 - val_loss: 0.0646 - learning_rate: 0.0010\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7fee51bcb390>"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "# ----------------------------\n",
        "# Binary labels\n",
        "# ----------------------------\n",
        "y_binary_0 = (y_train == 0).astype(int)\n",
        "y_binary_1 = (y_train == 1).astype(int)\n",
        "y_binary_3 = (y_train == 3).astype(int)\n",
        "\n",
        "# ----------------------------\n",
        "# Class 0 XGBoost classifier\n",
        "# ----------------------------\n",
        "xgb_0 = XGBClassifier(\n",
        "    n_estimators=200,\n",
        "    max_depth=4,\n",
        "    scale_pos_weight=(len(y_binary_0) - y_binary_0.sum()) / y_binary_0.sum(),\n",
        "    use_label_encoder=False,\n",
        "    eval_metric='logloss',\n",
        "    random_state=42\n",
        ")\n",
        "xgb_0.fit(X_train_scaled, y_binary_0)\n",
        "\n",
        "# ----------------------------\n",
        "# Class 1 XGBoost classifier\n",
        "# ----------------------------\n",
        "xgb_1 = XGBClassifier(\n",
        "    n_estimators=200,\n",
        "    max_depth=4,\n",
        "    scale_pos_weight=(len(y_binary_1) - y_binary_1.sum()) / y_binary_1.sum(),\n",
        "    use_label_encoder=False,\n",
        "    eval_metric='logloss',\n",
        "    random_state=42\n",
        ")\n",
        "xgb_1.fit(X_train_scaled, y_binary_1)\n",
        "\n",
        "# ----------------------------\n",
        "# Class 3 XGBoost classifier (now WITH scale_pos_weight)\n",
        "# ----------------------------\n",
        "xgb_3 = XGBClassifier(\n",
        "    n_estimators=200,\n",
        "    max_depth=4,\n",
        "    learning_rate=0.1,\n",
        "    scale_pos_weight=(len(y_binary_3) - y_binary_3.sum()) / y_binary_3.sum(),\n",
        "    use_label_encoder=False,\n",
        "    eval_metric='logloss',\n",
        "    random_state=42\n",
        ")\n",
        "xgb_3.fit(X_train_scaled, y_binary_3)\n",
        "\n",
        "# ----------------------------\n",
        "# Predict probabilities\n",
        "# ----------------------------\n",
        "y_bin_0 = xgb_0.predict_proba(X_test_scaled)[:, 1]  # Probability of class 0\n",
        "y_bin_1 = xgb_1.predict_proba(X_test_scaled)[:, 1]  # Probability of class 1\n",
        "y_bin_3 = xgb_3.predict_proba(X_test_scaled)[:, 1]  # Probability of class 3\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SpuoL0iRDfoi",
        "outputId": "e67ced4b-06d8-44ff-9c3a-8119e9a20c45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [02:09:00] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [02:09:19] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n",
            "/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [02:09:37] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"use_label_encoder\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
        "\n",
        "# ----------------------------\n",
        "# CNN predictions\n",
        "# ----------------------------\n",
        "y_cnn_proba = model.predict(X_test_cnn, verbose=0)\n",
        "y_cnn_pred = np.argmax(y_cnn_proba, axis=1)\n",
        "\n",
        "# ----------------------------\n",
        "# XGBoost binary classifier probabilities\n",
        "# ----------------------------\n",
        "y_bin_0 = xgb_0.predict_proba(X_test_scaled)[:, 1]  # Probability of class 0\n",
        "y_bin_1 = xgb_1.predict_proba(X_test_scaled)[:, 1]  # Probability of class 1\n",
        "y_bin_3 = xgb_3.predict_proba(X_test_scaled)[:, 1]  # Probability of class 3\n",
        "\n",
        "# ----------------------------\n",
        "# Class 1 - ROC Curve and Threshold\n",
        "# ----------------------------\n",
        "y_true_binary_1 = (y_test == 1).astype(int)\n",
        "\n",
        "fpr_1, tpr_1, thresholds_1 = roc_curve(y_true_binary_1, y_bin_1)\n",
        "target_recall_1 = 0.90\n",
        "index_1 = np.argmax(tpr_1 >= target_recall_1)\n",
        "\n",
        "if index_1 < len(thresholds_1):\n",
        "    best_threshold_1 = thresholds_1[index_1]\n",
        "else:\n",
        "    best_threshold_1 = thresholds_1[-1]  # fallback\n",
        "\n",
        "print(f\"\\nBest threshold for class 1 with recall ≥ {target_recall_1}: {best_threshold_1:.4f}\")\n",
        "\n",
        "# Optional: Diagnostic metrics for class 1\n",
        "y_pred_binary_1 = (y_bin_1 > 0.5).astype(int)\n",
        "print(\"\\nXGBoost Binary Classifier for Class 1 vs Others:\")\n",
        "print(classification_report(y_true_binary_1, y_pred_binary_1, digits=4))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_true_binary_1, y_pred_binary_1))\n",
        "print(\"ROC AUC Score (Class 1):\", round(roc_auc_score(y_true_binary_1, y_bin_1), 4))\n",
        "\n",
        "# ----------------------------\n",
        "# Class 3 - ROC Curve and Threshold\n",
        "# ----------------------------\n",
        "y_true_binary_3 = (y_test == 3).astype(int)\n",
        "\n",
        "fpr_3, tpr_3, thresholds_3 = roc_curve(y_true_binary_3, y_bin_3)\n",
        "target_recall_3 = 0.90\n",
        "index_3 = np.argmax(tpr_3 >= target_recall_3)\n",
        "\n",
        "if index_3 < len(thresholds_3):\n",
        "    best_threshold_3 = thresholds_3[index_3]\n",
        "else:\n",
        "    best_threshold_3 = thresholds_3[-1]\n",
        "\n",
        "print(f\"\\nBest threshold for class 3 with recall ≥ {target_recall_3}: {best_threshold_3:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vz5DyFgOGPI4",
        "outputId": "ba7bae32-eefd-40b9-9391-7b3271cf0db7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Best threshold for class 1 with recall ≥ 0.9: 0.0626\n",
            "\n",
            "XGBoost Binary Classifier for Class 1 vs Others:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9946    0.9914    0.9930     21336\n",
            "           1     0.7067    0.7932    0.7475       556\n",
            "\n",
            "    accuracy                         0.9864     21892\n",
            "   macro avg     0.8507    0.8923    0.8702     21892\n",
            "weighted avg     0.9873    0.9864    0.9868     21892\n",
            "\n",
            "Confusion Matrix:\n",
            "[[21153   183]\n",
            " [  115   441]]\n",
            "ROC AUC Score (Class 1): 0.9605\n",
            "\n",
            "Best threshold for class 3 with recall ≥ 0.9: 0.2426\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_preds = []\n",
        "\n",
        "for i in range(len(y_cnn_pred)):\n",
        "    cnn_class = y_cnn_pred[i]\n",
        "    cnn_conf = y_cnn_proba[i][cnn_class]\n",
        "\n",
        "    prob_class_1 = y_cnn_proba[i][1]\n",
        "    prob_class_3 = y_cnn_proba[i][3]\n",
        "\n",
        "    bin_prob_0 = y_bin_0[i]\n",
        "    bin_prob_1 = y_bin_1[i]\n",
        "    bin_prob_3 = y_bin_3[i]\n",
        "\n",
        "    # --- Override to Class 1 if both classifiers agree ---\n",
        "    if cnn_class != 1 and prob_class_1 > 0.45 and bin_prob_1 > best_threshold_1:\n",
        "        final_preds.append(1)\n",
        "\n",
        "    # --- Override to Class 3 if both classifiers agree ---\n",
        "    elif cnn_class != 3 and prob_class_3 > 0.4 and bin_prob_3 > best_threshold_3:\n",
        "        final_preds.append(3)\n",
        "\n",
        "    # --- Otherwise, keep original CNN prediction ---\n",
        "    else:\n",
        "        final_preds.append(cnn_class)\n",
        "\n",
        "final_preds = np.array(final_preds)\n"
      ],
      "metadata": {
        "id": "c9iL4KLWDlHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "print(\"Hybrid Ensemble with Class 0, Class 1 & Class 3 Reinforcement (Updated):\")\n",
        "print(classification_report(y_test, final_preds))\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, final_preds))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toStPY4BDpLi",
        "outputId": "01679561-3707-4917-95d2-420d80606dd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hybrid Ensemble with Class 0, Class 1 & Class 3 Reinforcement (Updated):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95     18118\n",
            "           1       0.38      0.92      0.53       556\n",
            "           2       0.89      0.95      0.92      1448\n",
            "           3       0.21      0.93      0.34       162\n",
            "           4       0.96      0.99      0.97      1608\n",
            "\n",
            "    accuracy                           0.92     21892\n",
            "   macro avg       0.69      0.94      0.74     21892\n",
            "weighted avg       0.97      0.92      0.94     21892\n",
            "\n",
            "Confusion Matrix:\n",
            "[[16543   828   165   511    71]\n",
            " [   31   510     7     6     2]\n",
            " [   11    10  1377    48     2]\n",
            " [    3     4     4   151     0]\n",
            " [    3     3     1     2  1599]]\n"
          ]
        }
      ]
    }
  ]
}